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We're teaching an in-person LLM bootcamp in the SF Bay Area on November 14, 2023. Come join us if you want to see the most up-to-date materials building LLM-powered products and learn in a hands-on environment.

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Hope to see some of you there!

--------------------------------------------------------------------------------------------- What are effective ways to label your data?
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Summary
- Data labeling requires a collection of data points such as images, text, or audio and a qualified team of people to label each of the input points with meaningful information that will be used to train a machine learning model.
- You can create a user interface with a standard set of features (bounding boxes, segmentation, key points, cuboids, set of applicable classes…) and train your own annotators to label the data.
- You can leverage other labor sources by either hiring your own annotators or crowdsourcing the annotators.
- You can also consult standalone service companies. Data labeling requires separate software stack, temporary labor, and quality assurance; so it makes sense to outsource.

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